Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier
Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that
is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D�…
is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D�…
A unified visual information preservation framework for self-supervised pre-training in medical image analysis
Recent advances in self-supervised learning (SSL) in computer vision are primarily
comparative, whose goal is to preserve invariant and discriminative semantics in latent�…
comparative, whose goal is to preserve invariant and discriminative semantics in latent�…
DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images
Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive
to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature�…
to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature�…
Self-supervised pre-training of swin transformers for 3d medical image analysis
Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream�…
learning of global and local representations that can be transferred to downstream�…
Anatomical invariance modeling and semantic alignment for self-supervised learning in 3d medical image analysis
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally�…
image analysis tasks. Most current methods follow existing SSL paradigm originally�…
Emp-ssl: Towards self-supervised learning in one training epoch
Recently, self-supervised learning (SSL) has achieved tremendous success in learning
image representation. Despite the empirical success, most self-supervised learning methods�…
image representation. Despite the empirical success, most self-supervised learning methods�…
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher…�- Proceedings of the�…, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
Benchmarking self-supervised learning on diverse pathology datasets
Computational pathology can lead to saving human lives, but models are annotation hungry
and pathology images are notoriously expensive to annotate. Self-supervised learning has�…
and pathology images are notoriously expensive to annotate. Self-supervised learning has�…
Geometric visual similarity learning in 3d medical image self-supervised pre-training
Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic�…
due to their sharing of numerous same semantic regions. However, the lack of the semantic�…
Big self-supervised models advance medical image classification
Self-supervised pretraining followed by supervised fine-tuning has seen success in image
recognition, especially when labeled examples are scarce, but has received limited attention�…
recognition, especially when labeled examples are scarce, but has received limited attention�…